@article {PENG2020110, title = {Dendrite P systems}, journal = {Neural Networks}, volume = {127}, year = {2020}, pages = {110 - 120}, abstract = {It was recently found that dendrites are not just a passive channel. They can perform mixed computation of analog and digital signals, and therefore can be abstracted as information processors. Moreover, dendrites possess a feedback mechanism. Motivated by these computational and feedback characteristics, this article proposes a new variant of neural-like P systems, dendrite P (DeP) systems, where neurons simulate the computational function of dendrites and perform a firing{\textendash}storing process instead of the storing{\textendash}firing process in spiking neural P (SNP) systems. Moreover, the behavior of the neurons is characterized by dendrite rules that are abstracted by two characteristics of dendrites. Different from the usual firing rules in SNP systems, the firing of a dendrite rule is controlled by the states of the corresponding source neurons. Therefore, DeP systems can provide a collaborative control capability for neurons. We discuss the computational power of DeP systems. In particular, it is proven that DeP systems are Turing-universal number generating/accepting devices. Moreover, we construct a small universal DeP system consisting of 115 neurons for computing functions.}, keywords = {Computational power, Dendrite P systems, Neural-like P systems, P systems}, issn = {0893-6080}, doi = {https://doi.org/10.1016/j.neunet.2020.04.014}, url = {http://www.sciencedirect.com/science/article/pii/S0893608020301349}, author = {Hong Peng and Tingting Bao and Xiaohui Luo and Jun Wang and Xiaoxiao Song and Agust{\'\i}n Riscos-N{\'u}{\~n}ez and Mario J. P{\'e}rez-Jim{\'e}nez} }